Efficient management of end-of-life lithium-ion batteries (LIBs) is critical to advancing circular economy goals. To support informed decision-making for circular strategies (ReX) such as reuse, remanufacturing, repair, or recycling, this study proposes a systematic and adaptive framework D-BReX (Decision-Making Framework for Circularity and Assessment of ReX in Battery Life Cycles). The framework is designed to leverage structured data from the Digital Battery Passport (DBP) and integrates it with fuzzy Failure Mode and Effects Analysis (FMEA) and State of Health (SOH) assessments. This hybrid approach allows for risk- and condition-based evaluation at the pack, module, and cell levels, supporting scalable, safety-conscious ReX decisions. The framework includes cost modelling to quantify trade-offs between diagnostic thoroughness and operational efficiency. A case study on retired E-bike battery packs demonstrates how D-BReX adapts to varying failure profiles and supports optimal ReX strategies under different economic and safety constraints. The findings affirm that D-BReX is effective in guiding second-life battery decisions, offering a structured yet flexible approach aligned with circular economy (CE) goals. Its iterative structure also enables continuous enhancement of DBP data, improving traceability and long-term decision quality.
Electroplated zinc-nickel (Zn-Ni) coatings are commonly applied as corrosion protection for steel components in demanding industrial environments. However, due to high process complexity, coupled with a limited process transparency and irregular chemical monitoring, achieving resource efficiency and predictive control remains challenging. This paper presents a data-driven approach for monitoring and modelling of an alkaline Zn-Ni barrel plating process at the process and product levels using machine learning (ML) techniques. A methodology was developed within a cyber-physical production system framework for training the models based on a controlled laboratory environment (20 L bath volume) and knowledge transfer on an industrial electroplating plant (13,000 L bath volume). Different supervised algorithms were trained with a limited set of input features to predict metal concentrations of the bath, coating thickness, nickel ratio in the coating and aggregated coating quality indicator. Despite data scarcity and process variability, the selected best models demonstrate accurate prediction rates (>86%). The results show that experimentally generated data can improve product-level modelling in the industrial environment, while process-level transfer remains limited when relevant industrial interventions, such as zinc dosing, are not recorded.
Additive manufacturing (AM) of high-speed steel (HSS) is of great interest for the manufacture of optimized forming and cutting tools. This study aims to systematically develop the sinter-based Cold Metal Fusion process for M2 steel and enable manufacture of complex shaped part designs. Green parts were printed with surface roughness, R-a, of 21 mu m and density of 5.40 g/cm(3). A sintering temperature of 1248 degrees C yielded a sintered density of >99% theoretical density and significantly decreased surface roughness to 8-10 mu m. The microstructure consisted of martensite, and of grain boundary and finer intragranular MC and M6C carbides. As-sintered Vickers hardness was 576 + 10 HV5 and increased to 933+ 27 HV5 upon oil quenching. Compression tests after air quenching revealed superior compressive strength of >3360 MPa compared to the as-sintered and oil cooled HSS. The fracture mode changed from ductile transgranular fracture for the as-sintered material to brittle intergranular upon oil cooling. As-sintered tensile specimens showed low strength of 681 + 43 MPa and brittle intergranular fracture as opposed to the as-sintered compression specimens. Finally, the manufacturing capability for an injection molding tool insert was proven, as no defects were identified. This novel process will allow for the manufacture of complex high-performance parts with improved durability and efficiency.
Adhesion wear during blanking of aluminium is a well-known and industrially relevant problem, as adhesion formation is influenced by process-inherent thermoelectric currents arising from the Seebeck effect. Previous studies have shown that externally applied countercurrents can reduce adhesion by suppressing these thermoelectric currents. However, their application has been limited to time constant countercurrents whose magnitudes had to be calibrated through numerous experimental trials. In this work, a universally integratable compensation system enabling time-resolved countercurrents is introduced. Different process-driven countercurrent strategies are systematically compared, allowing the most effective strategy to be isolated. Based on this strategy, a linear calculation formula for the required countercurrent magnitude is established. Throughout the study, non-lubricated blanking of AA5754 (2.5 mm and 4.0 mm) was investigated. The calculation formula is experimentally validated by varying material thickness, cutting speed, and punch material. The proposed approach achieves a reduction of the mean adhesion height on the punch by 28% to 67%, while reducing the experimental effort by more than 85%.
Mechanical engineering, as a leading industry, offers the potential to increase resource and energy efficiency for the targeted implementation of sustainable value chains. The application of laser-based processes, especially laser-based additive manufacturing, enables the production of thin-walled, complex, and functionally integrated structures with a high stiffness-to-weight ratio, making them ideal for lightweight applications. However, a disadvantage of laser-based processes is the significantly higher energy demand compared to conventional methods. This suggests that manufacturing lightweight components while keeping the amount of additive manufacturing at a minimum should be environmentally favorable. The subject of the investigation is to test this hypothesis on an aluminum-based component from the automotive sector and its topology optimized counterpart. For that purpose, a hybrid manufacturing route—including laser cutting, laser welding, and laser-based additive manufacturing—is compared to two reference routes depicting conventional manufacturing and purely additive manufacturing by means of a life cycle assessment (LCA). The assessment is partly based on primary data from experiments, covers production and use phase and also considers future energy conditions through the incorporation of integrated assessment models in form of prospective LCA. The results show that minimizing the share of additive manufacturing within a topology-optimized component can reduce environmental impacts drastically. But compared to conventional manufacturing impact savings are still only achieved under future energy conditions.
